Ethanfel
d642255e70
Integrates GIMM-VFI alongside existing BIM/EMA/SGM models. Key feature: generates all intermediate frames in one forward pass (no recursive 2x passes needed for 4x/8x). - Vendor gimm_vfi_arch/ from kijai/ComfyUI-GIMM-VFI with device fixes - Two variants: RAFT-based (~80MB) and FlowFormer-based (~123MB) - Auto-download checkpoints from HuggingFace (Kijai/GIMM-VFI_safetensors) - Three new nodes: Load GIMM-VFI Model, GIMM-VFI Interpolate, GIMM-VFI Segment Interpolate - single_pass toggle: True=arbitrary timestep (default), False=recursive like other models - ds_factor parameter for high-res input downscaling Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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# This source code is licensed under the license found in the
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# LICENSE file in the root directory of this source tree.
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# --------------------------------------------------------
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# References:
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# ginr-ipc: https://github.com/kakaobrain/ginr-ipc
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# --------------------------------------------------------
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import math
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import torch
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import torch.nn as nn
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from .layers import Sine, Damping
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def convert_int_to_list(size, len_list=2):
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if isinstance(size, int):
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return [size] * len_list
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else:
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assert len(size) == len_list
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return size
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def initialize_params(params, init_type, **kwargs):
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fan_in, fan_out = params.shape[0], params.shape[1]
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if init_type is None or init_type == "normal":
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nn.init.normal_(params)
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elif init_type == "kaiming_uniform":
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nn.init.kaiming_uniform_(params, a=math.sqrt(5))
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elif init_type == "uniform_fan_in":
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bound = 1 / math.sqrt(fan_in) if fan_in > 0 else 0
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nn.init.uniform_(params, -bound, bound)
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elif init_type == "zero":
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nn.init.zeros_(params)
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elif "siren" == init_type:
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assert "siren_w0" in kwargs.keys() and "is_first" in kwargs.keys()
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w0 = kwargs["siren_w0"]
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if kwargs["is_first"]:
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w_std = 1 / fan_in
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else:
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w_std = math.sqrt(6.0 / fan_in) / w0
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nn.init.uniform_(params, -w_std, w_std)
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else:
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raise NotImplementedError
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def create_params_with_init(
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shape, init_type="normal", include_bias=False, bias_init_type="zero", **kwargs
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):
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if not include_bias:
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params = torch.empty([shape[0], shape[1]])
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initialize_params(params, init_type, **kwargs)
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return params
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else:
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params = torch.empty([shape[0] - 1, shape[1]])
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bias = torch.empty([1, shape[1]])
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initialize_params(params, init_type, **kwargs)
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initialize_params(bias, bias_init_type, **kwargs)
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return torch.cat([params, bias], dim=0)
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def create_activation(config):
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if config.type == "relu":
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activation = nn.ReLU()
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elif config.type == "siren":
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activation = Sine(config.siren_w0)
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elif config.type == "silu":
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activation = nn.SiLU()
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elif config.type == "damp":
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activation = Damping(config.siren_w0)
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else:
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raise NotImplementedError
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return activation
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